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Record W2132107764 · doi:10.3109/10826084.2013.800119

Changes in the Characteristics and Levels of Comorbidity Among New Patients Into Methadone Maintenance Treatment Program in British Columbia During Its Expansion Period From 1998–2006

2013· article· en· W2132107764 on OpenAlexafffundabout
Behnam Sharif, Bohdan Nosyk, Huiying Sun, David C. Marsh, Aslam H. Anis

Bibliographic record

VenueSubstance Use & Misuse · 2013
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsNOSM UniversityAIDS VancouverHIV Legal NetworkUniversity of British Columbia
FundersNational Institute on Drug AbuseCanadian Institutes of Health Research
KeywordsComorbidityConfoundingMethadone maintenanceOddsMedicineMethadoneNational Comorbidity SurveyDemographyPsychiatryGerontologyInternal medicineLogistic regression

Abstract

fetched live from OpenAlex

We described the changing characteristics and comorbidity levels of new patients into Methadone maintenance treatment (MMT) program in British Columbia, Canada, during its expansion period of 1998-2006. Analyses used administrative data. Generalized regression models were applied using Charlson Comorbidity Index (CCI) and Chronic Disease Score (CDS) as outcomes. 12,615 individuals initiated MMT during 1998-2006, while their odds of having moderate CCI (1 ≤ CCI ≤ 4) and mean CDS increased by 60% and 11%, respectively, after adjusting for confounders. MMT entrants were presented with progressively higher levels of comorbidity, independent of other characteristics. Future MMT policies should address higher levels of comorbidity among new patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.274
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2013
Admission routes3
Has abstractyes

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